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Grape Guard:A YOLO-based mobile application for detecting grape leaf diseases

Journal of Electronic Science and Technology 2025年第1期23卷 60-75页

作者： Sajib Bin Mamun Israt Jahan Payel Md.Taimur Ahad Anthony S.Atkins Bo Song Yan Li Department of Computer Science and Engineering Daffodil International UniversityDhaka1207Bangladesh Department of Computer Science University of Southern QueenslandToowoomba4350Australia Faculty of Digital TechnologyInnovationand BusinessStaffordshire UniversityStoke-on-TrentST42DEUnited Kingdom School of Engineering University of Southern QueenslandToowoomba4350Australia School of Mathematics Physics and ComputingUniversity of Southern QueenslandToowoomba4350Australia

Grape crops are a great source of income for *** yield and quality of grapes can be improved by preventing and treating *** farmer’s yield will be dramatically impacted if diseases are found on grape *** detection can reduce the chances of leaf diseases affecting other healthy *** studies have been conducted to detect grape leaf diseases,but most fail to engage with end users and integrate the model with real-time mobile *** study developed a mobile-based grape leaf disease detection(GLDD)application to identify infected leaves,Grape Guard,based on a TensorFlow Lite(TFLite)model generated from the You Only Look Once(YOLO)v8 model.A public grape leaf disease dataset containing four classes was used to train the *** results of this study were relied on the YOLO architecture,specifically YOLOv5 and *** extensive experiments with different image sizes,YOLOv8 performed better than ***8 achieved 99.9%precision,100%recall,99.5%mean average precision(mAP),and 88%mAP50-95 for all classes to detect grape leaf *** Grape Guard android mobile application can accurately detect the grape leaf disease by capturing images from grape vines.

关键词： Bacterial diseases Grape Guard Mobile-based application YOLOv5 YOLOv8

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Coastal and Rural Digital Exclusion: The Case for Voice AI

Coastal and Rural Digital Exclusion: The Case for Voice AI

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Baxter, Rory Hagen, Oksana Aly, Amir Jones, Ray B. Willis, Katharine School of Nursing and Midwifery University of Plymouth Plymouth United Kingdom School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom School of Art Design and Architecture University of Plymouth Plymouth United Kingdom

The adoption of digital platforms for health and social resources disadvantages vulnerable populations, including older adults and those living in remote or deprived areas. This could be remediated using voice-based conversational AI (Voice AI) systems delivered via landline phone, bypassing the requirement for digital device access or digital skills. British rural and coastal regions often have poorer digital infrastructure and pockets of deprivation, and consequently higher levels of digital exclusion. This study explores digital exclusion in Southwest England and the suitability of Voice AI systems for supporting digital inclusion. Seventeen participants aged 50 years or over were interviewed by telephone and took part in one of two face to face focus groups to identify how digital exclusion impacts access to health and wellbeing resources. The results indicated that digital access was severely impacted by unreliable infrastructure and exacerbated by limited digital skills. Phone-based Voice AI systems could then provide viable solutions to support access to digital health and social resources for digitally marginalised coastal and rural communities. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Electronic health record

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Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning

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Computers, Materials & Continua 2025年第1期82卷 1129-1150页

作者： Shijie Tang Yong Ding Huiyong Wang School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin541004China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic TechnologyGuilin541004China Institute of Cyberspace Technology HKCT Institute for Higher EducationHong Kong999077China School of Mathematics&Computing Science Guilin University of Electronic TechnologyGuilin541004China

As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are *** key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time *** address this issue,we propose an anomaly detection method based on distributed deep *** method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete *** use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation *** method can not only detect abnormal attacks but also locate the sensors that cause *** conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public *** experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.

关键词： Anomaly detection CPS deep learning MLP(multi-layer perceptron)

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MSRP-TODNet: a multi-scale reinforced region wise analyser for tiny object detection

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BMC Research Notes 2025年第1期18卷 1-11页

作者： Bikku, Thulasi Sree, K. P. N. V. Satya Thota, Srinivasarao Kumar, Malligunta Kiran Shanmugasundaram, P. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati 522503 India Department of Computer Science and Engineering Usha Rama College of Engineering and Technology Telaprolu India Department of Mathematics Amrita School of Engineering Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati 522503 India Department of Electrical and Electronics Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Mathematics College of Natural & Computational Sciences MizanTepi University 21 Tepi Bushira Ethiopia

Objective: Detecting small, faraway objects in real-time surveillance is challenging due to limited pixel representation, affecting classifier performance. Deep Learning (DL) techniques generate feature maps to enhance detection, but conventional methods suffer from high computational costs. To address this, we propose Multi-Scale Region-wise Pixel Analysis with GAN for Tiny Object Detection (MSRP-TODNet). The model is trained and tested on VisDrone VID 2019 and MS-COCO datasets. First, images undergo two-fold pre-processing using Improved Wiener Filter (IWF) for artifact removal and Adjusted Contrast Enhancement Method (ACEM) for blurring correction. The Multi-Agent Reinforcement Learning (MARL) algorithm splits the pre-processed image into four regions, analyzing each pixel to generate feature maps. These are processed by the Enhanced Feature Pyramid Network (EFPN), which merges them into a single feature map. Finally, a Generative Adversarial Network (GAN) detects objects with bounding boxes. Results: Experimental results on the DOTA dataset demonstrate that MSRP-TODNet outperforms existing state-of-the-art methods. Specifically, it achieves an mAP @0.5 of 84.2%, mAP @0.5:0.95 of 54.1%, and an F1-Score of 84.0%, surpassing improved TPH-YOLOv5, YOLOv7-Tiny, and DRDet by margins of 1.7%–6.1% in detection performance. These results demonstrate the framework’s effectiveness for accurate, real-time small object detection in UAV surveillance and aerial imagery. © The Author(s) 2025.

关键词： And feature pyramid network (FPN) Deep learning (DL) Pre-processing Reinforcement learning (RL) Small object detection

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

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Machine Learning-Powered Intrusion Detection for Network Security 12th

Machine Learning-Powered Intrusion Detection for Network Sec...

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12th International Conference on Recent Trends in computing, ICRTC 2024

作者： Nandish, H.D. Rahul, R. Dasari, Hari Charan Kumaran, U. Umah, B. Nagarajan, Senthil Murugan Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Mathematics Faculty of Science Technology and Medicine University of Luxembourg Esch-sur-AlzetteL-4365 Luxembourg

ISBN: (纸本)9789819788354

In today's digital age, network security is paramount. This paper introduces a robust web application designed to swiftly identify network attacks and real-time anomalies, enabling users to protect their networks effectively. By leveraging cutting-edge technologies and machine learning, our system provides insights into network accuracy, F1-score, and precision, allowing users to gauge their network's safety. Our intrusion detection system classifies attacks into four categories: Denial of Service (DoS), Probe, Remote-to-User (R2L), and User-to-Root (U2R). Positioned strategically within the network, it monitors data traffic from all connected devices and promptly responds to suspicious activity, ensuring network security. Users receive instant alerts to take immediate action. Key technologies include data preprocessing (LabelEncoder and MinMaxScaler), user-friendly web interface (MERN stack), and enhanced security with Google Earth 2.0 for authentication and MongoDB for secure data storage. The Python-based backend operates on the NSL-KDD dataset, facilitating effective intrusion detection evaluation. To enhance accuracy, our system employs four machine learning algorithms: K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), Random Forest, and Long Short-Term Memory (LSTM), offering comprehensive protection against network threats. In summary, this paper presents a potent network intrusion detection system for real-time threat identification and security assessment. By combining advanced technologies and machine learning, it ensures accuracy and reliability, serving as a valuable tool for network administrators and security professionals in safeguarding digital networks from evolving threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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COMPLEX DYNAMICS OF A HOST-VECTOR DYNAMICS OF DENGUE INFECTION INCORPORATING OPTIMAL CONTROL STRATEGY WITH COST-EFFECTIVENESS: A FRACTIONAL-ORDER DERIVATIVE METHOD

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Journal of Mathematical Sciences (United States) 2025年 1-30页

作者： Patra, Sathi Jana, Soovoojeet Adak, Sayani Majee, Suvankar Kar, T.K. Department of Mathematics Indian Institute of Engineering Science and Technology Shibpur West Bengal Howrah 711103 India Department of Mathematics Ramsaday College Amta West Bengal Howrah 711401 India Centre for Soft Computing Research Indian Statistical Institute West Bengal Kolkata 700108 India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Karnataka Bengaluru 560035 India

Dengue is a resurging ailment infecting an estimated 100–140 million individuals annually. To understand the transmission mechanism of dengue, we define a nine-compartmental (six compartments for humans and three compartments for vectors) model incorporating fractional-order derivatives. Three different control measures, namely insecticide control for mosquitoes, treatment, and public awareness campaigns for the human population, are considered for the mathematical model to be proposed. We assess the existence and uniqueness of the solution and calculate the basic reproduction number (R0) using the next-generation matrix approach. It has been established that the proposed system has two equilibria viz: disease-free and endemic equilibrium, depending on R0. The stability of these equilibria has been investigated, considering R0 as the threshold parameter. Also, we have found that at R0=1, the system produces a transcritical bifurcation. Through numerical simulation with feasible parameter values, we demonstrate that employing controls decreases the infection, and all controls lead to a quicker removal of the disease. A cost-effectiveness analysis has been performed utilizing infection averted ratio (IAR) and incremental cost-effectiveness ratio (ICER) methods to determine the most cost-effective policy. Finally, the model has been calibrated using real dengue cases in Cambodia, one of the most dengue-prone countries, resulting in short-term predictions showing that the fractional-order system is more appropriate. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cost-effectiveness analysis Dengue Fractional-order calculus Short-term prediction of dengue Transcritical bifurcation

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Enhancing Milk Yield Forecasting in Dairy Farming Using an Interpretable Machine Learning Framework 4

Enhancing Milk Yield Forecasting in Dairy Farming Using an I...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Srinithi, B. Sruthi Nirmala, S.R. Thangavel, Senthil Kumar Somasundaram, K. Ramasamy, M. Amrita School of Physical Sciences Coimbatore Department of Mathematics Amrita Vishwa Vidyapeetham Tamil Nadu India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India Icarkvk Virinjipuram TanilNadu Vellore India

ISBN: (纸本)9798331523923

This study employs machine learning techniques and explainable AI (XAI) to enhance milk yield predictions and optimizes dairy farming practices. Split k-means clustering, KNN, SVM combined with k-means, and regression techniques such as binomial, polynomial, and logistic regression are all used in this methodology. KNN in conjunction with SVM improves prediction accuracy even further. Explainable AI techniques, such as SHAP, provide clarity by emphasizing feature significance, while diverse representations improve data scrutiny. These methods offer practical insights into improving dairy farming operations, ensuring efficient resource utilization, and increasing milk output. © 2025 IEEE.

关键词： Adversarial machine learning

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Fashion Design Transformation using Text-Guided Diffusion Models 4

Fashion Design Transformation using Text-Guided Diffusion Mo...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Adityan, Arjun Kabeleswar Vaiapury, Karthikeyan Palanisamy, T. Tangavel, Senthil Kumar Amrita Vishwa Vidyapeetham Department of Mathematics Coimbatore India Iitm Research Park Tcs Research and Innovation Chennai India Amrita School of Physical Sciences Department of Mathematics Amrita Vishwa Vidyapeetham Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331523923

We provide a novel approach for altering existing fashion designs through the integration of text-guided diffusion models with perceptual loss optimization. Effective technology for design evolution and modification is crucial for the rapid design iteration and prototyping of the fashion industry. We propose a pipeline that combines a LoRA-fine-tuned Stable Diffusion model with a perceptual loss to guarantee structural and stylistic faithfulness. The initial part is training a dedicated text-to-image generation model using fashion-specific data, subsequently refining the outputs through a perceptual loss mechanism to maintain structural integrity and improve stylistic coherence. After training with the DeepFashion-MultiModal dataset and evaluating performance on a custom dataset of essential garments, our method demonstrates exceptional preservation of stylistic coherence and structural integrity. Our approach offers a viable alternative for the automated transformation of fashion design while enhancing creative workflows within the fashion sector. © 2025 IEEE.

关键词： Hosiery manufacture

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Astronomical Image Clustering Using Spectral, K-Means and Mean Shift

Astronomical Image Clustering Using Spectral, K-Means and Me...

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computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Vaishnavi Shivaji Mohite Krishna Gayatri Patra CH Hari Sankar Sarada Jayan Department of Computer Science Amrita School of Computing Bengaluru Department of Mathematics Amrita School of Engineering Bengaluru

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

By analyzing vast amounts of observational data through astronomical image analysis, you can gain valuable information about the structure of the universe. This paper presents a new method for clustering astronomical images that uses both spectral and K-means methods and also incorporates linear algebra and optimization principles. We propose a method that combines feature extraction techniques such as the Fourier transform with clustering algorithms to classify celestial objects based on their characteristics. The research examines the effectiveness of spectral clustering and K-means clustering in organizing astronomical images into meaningful groups, taking into account factors such as computer efficiency, clustering accuracy and interpretability. Experimenting with different datasets, including telescope images, we demonstrate the effectiveness of our approach in automating the classification of astronomical objects. Our results reveal insights into the underlying structure of astronomical knowledge and offer promising avenues for future research in computational astronomy. In general, this research advances both the field of astronomy and the application of mathematical methods to image analysis and clustering.

关键词： Image analysis Fourier transforms Accuracy Heuristic algorithms Clustering algorithms Linear algebra Feature extraction Optimization Astronomy Tuning

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